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Artificial Intelligence. Dr. Paul Wagner Department of Computer Science University of Wisconsin – Eau Claire. Messages. Artificial Intelligence (AI) is an interesting sub-field of computer science that provides many contributions to the overall field

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Artificial Intelligence

Dr. Paul Wagner

Department of Computer Science

University of Wisconsin – Eau Claire


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Messages

  • Artificial Intelligence (AI) is an interesting sub-field of computer science that provides many contributions to the overall field

  • CS 420, as the AI course at UWEC, is a good opportunity to begin to explore these issues


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Outline

  • Overview

  • AI Topics

    • Knowledge representation

    • Problem solving and search space manipulation

    • Planning

    • Learning

    • Communicating

    • Uncertainty

    • Intelligent agents

    • Robotics

  • AI Languages

  • MICS Robot Contest Video


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Overview of Artificial Intelligence

  • Definitions – four major combinations

    • Based on thinking or acting

    • Based on activity like humans or performed in rational way


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AI Definitions

  • Acting Humanly

    • Turing Test – computer passes test if a human interrogator asking written questions can distinguish written answers from computer or human

    • Computer needs:

      • Natural language processing

      • Knowledge representation

      • Automated reasoning

      • Machine learning


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AI Definitions (2)

  • Total Turing Test – includes video component (to test subject’s perceptual abilities) and opportunity to pass physical objects to subject

  • Computer also needs:

    • Computer vision

    • Robotics


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AI Definitions (3)

  • Thinking Humanly

    • Cognitive Modeling approach to AI

    • Involves crossover between computer science and psychology – cognitive science

    • Areas of interest

      • Cognitive models

      • Neural networks


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AI Definitions (4)

  • Thinking Rationally

    • “Laws of thought” approach to AI

    • Goal: solve any problem based on logical manipulation

    • Problems

      • Difficult to represent certain types of knowledge (e.g. common sense, informal knowledge)

      • Difference between solving problems in principle and in practice

        • E.g. computational limits


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AI Definitions (4)

  • Acting Rationally

    • “Design a rational agent” approach to AI

    • Advantages over logic approach

      • Logic is only one tool or many that can be used to design rational agent

      • Scientific advances can provide more tools for developing better agents


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Knowledge Representation

  • How to represent information?

  • Generally, we use some sort of tree, grid or network

  • Options

    • OO programming languages: classes/objects

    • Relational database system: tables/rows/columns

  • Problem

    • The world is more varied, with many types of things to represent


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Knowledge Representation (2)

  • Abstract Objects

    • Sets

    • Sentences

    • Measurements

      • Times

      • Weights

  • Generalized Events

    • Intervals

    • Places

    • Physical Objects

    • Processes


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Knowledge Representation (3)

  • Some things are very difficult to represent

    • Common sense

      • See http://www.cyc.com/

    • Combinations of multiple types

  • Issues of:

    • Type

    • Scale

    • Granularity

    • Combination

  • Other Questions

    • How to distinguish knowledge and belief?

    • What is the best way to reason with this information?


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Problem Solving and Search Space Manipulation

  • Many Algorithmic Approaches to Problem Solving

    • Depth-First Search

    • Breadth-First Search

  • Variations

    • Depth-Limited Search

    • Iterative Deepening Depth-First Search

    • Bi-directional Search


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Problem Solving and Search Space Manipulation (2)

  • Smarter Search

    • Greedy best-first search

    • A* search (combine costs of path so far plus path from current node to goal)

    • Memory-bounded heuristic search

      • Heuristic – means of estimating a measurement such as cost of search


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Problem Solving and Search Space Manipulation (3)

  • Issues

    • Avoiding repeated search

    • Searching with partial information


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Problem Solving and Search Space Manipulation (4)

  • Adversarial Search

    • E.g. games and game trees

    • Minimax algorithm

    • Alpha-Beta pruning


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Problem Solving and Search Space Manipulation (5)

  • Applications of Problem Solving

    • Expert Systems

      • Approximating the functionality of an absent human expert

    • Robotics

      • Encountering unexpected obstacles


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Planning

  • Many types of problems

    • “Blocks world”

    • Getting yourself from Eau Claire to the AAAI conference in Boston

    • Changing a flat tire

    • Completing all of your projects at the end of the semester

    • Developing a large software application


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Planning (2)

  • Approaches

    • State-based search

    • Partial-order planning

    • Planning graphs

  • Issues

    • Time

    • Scheduling

    • Resources


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Learning

  • Definition - Building on current knowledge by using experience to improve a system

  • Various approaches

    • Supervised/unsupervised/reinforcement

  • Forms of learning algorithms

    • Inductive logic

      • Example: given a set of point, approximate a line

    • Decision tree (set of questions, act differently depending on answer)


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Learning (2)

  • Issues

    • Computational Learning Theory

      • Intersection of theoretical CS, AI, statistics

    • How many examples do you need?


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Communicating

  • Major issue - Natural language processing

    • Many issues

      • Syntax

      • Semantics

      • Context

    • Steps

      • Perception

      • Parsing

      • Analysis

      • Disambiguation

      • Incorporation


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Uncertainty

  • Much knowledge is not absolute

    • Boundary between knowledge and belief is gray

  • Techniques for dealing with uncertainty

    • Probabilistic reasoning

    • Probabilistic reasoning over time

    • Fuzzy sets / fuzzy logic

    • Simple decision-making (evaluating utility)

    • Complex decision-making (taking ability to reevaluate into account)

  • Applications

    • Expert systems


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Intelligent Agents

  • Everything we’ve talked about can be viewed in terms of embedding intelligence within an agent

    • Software system

    • Machine with embedded software

    • Robot


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Intelligent Agents (2)

  • Issues for agents

    • Limitations on memory

    • Perceiving its environment

    • Working with other agents

    • Affecting its environment (through actuators)

  • Processes

    • Simple – based on rules

    • Complex – based on multiple pieces of logic, dealing with uncertainty


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Robotics

  • Field encompassing elements of computer science/AI, engineering, physical systems

  • Issues

    • Many that we’ve discussed, plus:

    • Perception

    • Actuation

  • Recent successes

    • Worker bots (e.g. floor cleaners)

    • Intelligent navigation (DARPA vehicle contest)

  • Test environments

    • Lego Mindstorms

    • Other robot packages or custom systems


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AI Languages

  • Scheme / LISP

    • Functional

    • Simple knowledge representation (list)

    • Easy to apply functionality to represented elements

  • Prolog

    • Logic-based

    • Facts and rules easily represented

    • Built-in search engine

  • Specialized languages

    • Rule languages (e.g. CLIPS)

    • Planning languages (e.g. STRIPS)


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CS 420

  • Spring semester, about every other year

  • Will be offered Spring 2007

  • Prerequisite: CS 330 (to get Scheme and Prolog background)

  • Topics

    • All of the above!


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CS 420 (2)

  • Possible Projects

    • Neural network to simulate decision making, natural language processing

    • Software development planning through cooperating intelligent agents

    • Expert system for deciding which courses to take to complete a CS major

    • Sumo robots?


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MICS Robot Contest Video

  • http://video.google.com/videoplay?docid=7851913746457357108&hl=en


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